Abstract

Parkinson’s disease (PD) and Alzheimer’s diseases are the most common brain diseases. Parkinson’s disease (PD) occurs when the neurons that produce dopamine in the brain are damaged. People aged 50 or above mostly suffer from Parkinson’s disease. PD and Alzheimer’s disease can be diagnosed by many different signals such as EEG and Speech signals. This paper proposes a method for detecting PD and Alzheimer’s disease where, discrete wavelet transform feature extraction technique were used and SVM network is used for classification. The accuracy of 91.6% is obtained.

References

• Chunchu Rambabu, B Rama Murthy,” EEG Signal with Feature Extraction using SVM and ICA Classifiers”, International Journal of Computer Applications (0975 – 8887)Volume 85 – No 3, January 2014.
• Marius Ene,” Neural network-based approach to discriminate healthy people from those with Parkinson's disease”, Annals of the University of Craiova, Math. Comp. Sci. Ser.Volume 35, 2008
• Patrizio Campisi, Senior Member, IEEE, and Daria La Rocca, Student Member, IEEE,” Brain Waves for Automatic Biometric-Based User Recognition”, IEEE transactions on information forensics and security, vol. 9, no. 5, may 2014.
• Saloni, R. K. Sharma and Anil K. Gupta,” Artificial Neural Networks and Support Vector Machines for Parkinson Disease Detection using Human Voice”, Advances in Circuits, Systems, Signal Processing and Telecommunications2015
• A.M. Ardi Handojoseno, James M. Shine, Tuan N. Nguyen, Member, IEEE, Yvonne Tran, Simon J.G. Lewis, Hung T. Nguyen, Senior Member, IEEE,” The detection of Freezing of Gait in Parkinson’s disease patients using EEG signals based on Wavelet Decomposition”,34thAnnual International Conference of the IEEE EMBS San Diego, California USA, 28 August - 1 September, 2012
• A.M. Ardi Handojoseno, James M. Shine, Tuan N. Nguyen, Member, IEEE,Yvonne Tran, Simon J.G. Lewis, Hung T. Nguyen, Senior Member, IEEE,” Using EEG Spatial Correlation, Cross Frequency Energy, and Wavelet Coefficients for the prediction of Freezing of Gait in Parkinson’s disease patients”,35th Annual International Conference of the IEEE EMBS Osaka, Japan, 3 - 7 July, 2013.
• Ingeborg H. Hansen, Mikkel Marcussen, Julie A. E. Christensen, Poul Jennum, and Helge B. D. Sorensen,” Detection of a Sleep Disorder Predicting Parkinson’s Disease”,35th Annual International Conference of the IEEE EMBS Osaka, Japan, 3 - 7 July, 2013.
• Mohammad Shahbakhi, and Danial Taheri Far and Ehsan Tahami2, “Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine”,J. Biomedical Science and Engineering, 2014, 7, 147-156.
• Max A. Little, Patrick E. McSharry, Eric J. Hunter, Jennifer Spielman, and Lorraine O. Ramig,” Suitability of Dysphonia Measurements for Telemonitoring of Parkinson’s Disease”, IEEE transactions on biomedical engineering, vol. 56, no. 4, april 2009.
• Mohammad Shahbakhti, Danial Taherifar, Zahra Zareei,” Combination Of PCA And SVM For Diagnosis Of Parkinson's Disease”, 2nd International Conference on Advances in Biomedical Engineering,2013.
• R. Arefi Shirvan, E. Tahami,” Voice Analysis for Detecting Parkinson’s Disease Using Genetic Algorithm and KNN Classification Method”, 18th Iranian Conference on BioMedical Engineering, 14-16 December 2011, Tehran, Iran.
• Sabato Santaniello, David L. Sherman, Nitish V. Thakor, “Optimal Control-based Bayesian detection of clinical ”, IEEE transactions on neural systems and rehabilitation engineering, vol. 20, no. 5, september 2012.
• Gertrud Laura Sorensen, Jacob Kempfn, Poul Jennum and Helge B. D. Sorense, “ Detection of arousals in Parkinson’s disease patients”,33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, August 30 - September 3, 2011
• H.H.Stassen,”Computerized recognition of persons by EEG spectral patterns”,Electroencephalography and ClinicalNeurophsiology,1980,49:190-194, Elsevier/North-Holland Scientific Publishers, Ltd.
• Lucas R. Trambaiolli, Tiago H. Falk, Francisco J. Fraga, Renato Anghinah, and Ana C. Lorena,”EEG Spectro-Temporal Modulation Energy : a New Feature for Automated Diagnosis of Alzheimer’s Disease”,33rd Annual International Conference of the IEEE EMBSBoston, Massachusetts USA, August 30 -September 3, 2011.
• Ashwini Nakate, P.D. Bahirgonde,” Feature Extraction of EEG Signal using Wavelet Transform”,International Journal of Computer Applications (0975 – 8887)Volume 124 – No.2, August 2015.
• M.A.Anusuya, S.K.Katti,” Speech Recognition by Machine: A Review”,(IJCSIS) International Journal of Computer Science and Information Security,Vol. 6, No. 3, 2009.
• P Bhuvaneswari, J Satheesh Kumar,” Support Vector Machine Technique for EEG Signals”,International Journal of Computer Applications (0975 – 8887) Volume 63– No.13, February 2013.
• Mohammad H. Alomari, Emad A. Awada, Aya Samaha1 and Khaled Alkamh,” Wavelet-Based Feature Extraction for the Analysis of EEG Signals associated with Imagined Fists and Feet Movements”, Computer and Information Science; Vol. 7, No. 2,2014.
• Alan W.C. Tan, M.V.C. Rao, B.S. Daya Sagar,”A signal subspace approach for speech modelling and classification”, ELSEVIER Signal Processing 87 (2007) 500–508,2006.
• Monica Mundada, Sangramsing Kayte, Sumegh Tharewal, Dr Bharti Gawali,”Classification of Fluent and Dysfluent Speech using KNN classifier”,International Journal of Advanced Research in Computer Science and Software Engineering 4(9), September - 2014, pp. 1-7.
• Hafeez Ullah Amin and Aamir Saeed Malik and Rana Fayyaz Ahmad.” Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques”, Australasian College of Physical Scientists and Engineers in Medicine,SPRINGER,2015.
• Parwinder Pal Singh and Pushpa Rani,” An Approach to Extract Feature using MFCC”,IOSR Journal of Engineering (IOSRJEN)Vol. 04, Issue 08 (August. 2014).
• Siddhant C. Joshi and Dr. A.N.Cheeran,” MATLAB Based Feature Extraction Using Mel Frequency Cepstrum Coefficients for Automatic Speech Recognition”,International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 6, June 2014.